.png){kind=icon}
返回
Virtual node graph neural network for full phonon prediction
.png){kind=icon}
PRE
AI
DOI：10.1038/s43588-024-00661-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Gamma-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Gamma-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.
Keyword:
DYNAMICS
DESIGN
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
分享收藏Sleep Duration and Cognition in a Nationally Representative Sample of U.S. Older Adults美国老年人全国代表性样本的睡眠时间和认知
OAAI
OAAI分享收藏Atomistic Line Graph Neural Network for improved materials property predictions用于改进材料性能预测的原子线图神经网络OAAI
OAAI分享收藏Metal-Organic Frameworks for Electrocatalysis: Catalyst or Precatalyst?用于电催化的金属有机骨架: 催化剂还是预催化剂？.png)
ACS ENERGY LETTERSOAAI
ACS ENERGY LETTERSIF18.2
OAAI分享收藏CHGNet as a pretrained universal neural network potential for charge-informed atomistic modellingCHGNet作为预训练的通用神经网络，具有用于电荷信息原子建模的潜力
OAAI
OAAI分享收藏Machine learning enabled autonomous microstructural characterization in 3D samples机器学习在3D样品中实现自主微观结构表征OAAI
OAAI分享收藏分享收藏Metal-Free Organic Dyes for Dye-Sensitized Solar Cells: From Structure: Property Relationships to Design Rules用于染料敏化太阳能电池的无金属有机染料: 从结构: 性能关系到设计规则
PREAI
分享收藏
查看更多内容